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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary Workshop Summary INTRODUCTION Despite spending more time and money in developing novel therapeutics, the success rate for new pharmacologic treatments has been poor. Although the research and development (R&D) expenditures have grown 13 percent each year since 1970 (a 50-fold increase), the number of new drugs approved annually is no greater now than it was 50 years ago (Booth and Zemmel, 2004; Munos, 2009). Over the past decade, skyrocketing costs and the complexity of the scientific knowledge upon which to develop new agents have provided incentives for alternative approaches to drug development, if we are to continue to improve clinical care and reduce mortality. These challenges create opportunities for improved collaboration between industry, academia, government, and philanthropic organizations at each stage in new drug development, marketing, and implementation. Perhaps the most appropriate initial step in addressing the need for collaboration is to consider more precompetitive relationships that allow sharing of scientific information to foster drug development. While these collaborative relationships in basic and preclinical research on drug targets and the early stages of clinical testing are acknowledged to be potentially important drivers for innovation and more rapid marketing of new agents, they also raise a number of concerns that must be addressed. For example, acknowledgment of academic productivity and independence and economic competitiveness must be considered and these
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary challenges managed to foster a culture of collaboration. At the same time, regulatory issues, the need for standardization, and intellectual property (IP) concerns must be confronted if the current models for drug development are to be refined to encourage robust participation in precompetitive collaborations. Recognizing the growing importance of precompetitive collaborations in oncology drug development, as well as the challenges these innovative collaborations pose, the National Cancer Policy Forum of the Institute of Medicine (IOM) held a workshop titled Extending the Spectrum of Precompetitive Collaboration in Oncology Research on February 9 and 10, 2010, in Washington, DC. At the workshop, speakers addressed: Current driving forces for precompetitive collaborations; Benefits of such collaborations; Challenges to collaborating; Types of precompetitive collaborations and what can be shared; Precompetitive collaboration examples; Lessons learned and best practices formulated from these examples of collaboration; and Next steps that could facilitate more precompetitive collaborations in oncology drug development. This document is a summary of the workshop proceedings. The views expressed in this summary are those of the speakers and discussants, as attributed to them, and are not the consensus views of the workshop participants or members of the National Cancer Policy Forum. Building on the National Cancer Policy Forum’s workshop, the IOM’s Roundtable on Translating Genomic-Based Research for Health held a related workshop on precompetitive collaboration July 22, 2010, titled Establishing Precompetitive Collaborations to Stimulate Genomics Driven Drug Development. A published summary of that workshop is also planned. CURRENT DRIVING FORCES FOR COLLABORATION John Wagner, vice president of clinical pharmacology at Merck & Co., began the workshop by pointing out that the notion of precompetitive collaboration is not new, nor is it limited to biomedical applications. A precompetitive collaboration launched by the semiconductor industry
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary in the 1980s (SEMATECH)1 boosted the global competitiveness of U.S. companies within this industry (see Box 1). The software industry is also known for its precompetitive collaborations, which Stephen Weber defined as “competitors sharing early stages of research that benefit all,” in his book The Success of Open Source (Weber, 2004). But Wagner said a number of factors are currently driving precompetitive collaborations in biomedicine, most notably the standard drug development model does not appear to be working very effectively. He presented a slide showing that the new molecular entity output per dollar spent on research and development has been declining since 1970 (see Figure 1). In addition, he cited a 2004 analysis of the success rates of compounds making it from first-in-human trials to registration during a 10-year period (1991–2000) for 10 large pharmaceutical companies. The average success rate for all therapeutic areas is approximately 11 percent; in oncology, the probability of graduating from the drug development pipeline and making it to market is only 5 percent (Kola and Landis, 2004). “This tees up the issue of the need for different models of doing research and development, including precompetitive collaborations,” Wagner said. Several speakers expanded on the shortcomings of current approaches to drug development, suggesting that alternative approaches will be required. Many of the speakers proposed precompetitive collaborations as an approach worthy of careful consideration. Many factors have made the standard model for developing drugs inadequate, they pointed out, including the growing complexity of research and far-ranging and uneven distribution of knowledge, patient variability that contributes to the uncertainty and low success rate, increasing emphasis on comparative effectiveness and evidence-based medicine, the increasingly long and expensive time lines of drug development, and declining research and development budgets. Increasing Complexity and Data Many speakers noted the growing complexity of basic and clinical research in oncology, much of which hinges on deciphering the intricate networks of molecular pathways involved in the formation and progression of various cancers, as well as predicting patients’ likely responses to treatments aimed at the targets within those networks. The increasing need to integrate 1 SEMATECH stands for SEmiconductor MAnufacturing TECHnology (http://www.sematech.org/).
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary BOX 1 SEMATECH SEMATECH (SEmiconductor MAnufacturing TECHnology) is a collaboration of semiconductor manufacturers that was established in 1987 with the goal of improving U.S. competitiveness of the semi-conductor industry in the global market. William Spencer, chair emeritus of SEMATECH, noted that semiconductors are the backbone of computing power that extends not just to personal computers, but to the microprocessors that are in most appliances, automobiles, and communication and entertainment devices. “The technology is important from the standpoint of [the semiconductor] business, but more so because it drives these other businesses by increasing productivity each year,” said Spencer. In the 1970s, the United States owned 70 percent of the semi-conductor market, but by the 1980s it was rapidly losing market share to other countries, including Japan. Recognizing this, SEMATECH was established as a research and development collaboration among the major U.S. manufacturers of semiconductors. Congress also hoped that improved semiconductor manufacturing would bolster the defense technology base and matched industrial funding through the Defense Advanced Research Projects Agency. Industry members initially were required to contribute 1 percent of their semiconductor sales revenue, with a minimum contribution of $1 million and maximum contribution of $15 million. By 1994, the United States had regained market leadership and SEMATECH was funded solely by the contributions of its members. Over time, SEMATECH’s membership grew to include international companies. A significant accomplishment that contributed to SEMATECH’s success, according to Spencer, was the creation of a long-term semi-conductor technology roadmap. This roadmap laid out the goals of the genetics, genomics, and proteomics into new drug development requires data repositories and much more sophisticated information technology (IT) to analyze data. The magnitude of these challenges, speakers noted, may necessitate greater collaboration to ensure access to broader expertise than is often available within a single company or academic institution. Stephen Friend, president of Sage Bionetworks, illustrated how advances in molecular biology have fueled an explosion of data in the past decade (see Figure 2). “We are going to be swimming in data until models
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary industry, the science barriers that were hampering their achievement, and ways to overcome those barriers. In addition, the financial success of SEMATECH hinged in part on its creation of a piece of equipment that is essential for the manufacture of semiconductors and that is still in use today. Under the umbrella of SEMATECH were about a dozen individual projects, each with a limited focus, such as lithography. There was an oversight committee composed of chief technical officers or the heads of manufacturing from participating companies. Innovations by participants in SEMATECH could only be patented by the specific originators if they shared these innovations royalty-free with all consortium members. Spencer remarked on the surprising willingness of semiconductor companies, which at the time were engaged in cut-throat competition with each other, to work cooperatively to do the research and development needed to propel the semiconductor industry in the United States forward. He attributed part of this willingness to leadership. The founding chief executive officer of SEMATECH, Robert Noyce, brought instant credibility to SEMATECH because of his technical contributions to the semiconductor industry and his success as an entrepreneur. Spencer noted that “three things—crisis, competitive companies coming together, and industry leadership, were essential to getting SEMATECH started.” Spencer concluded his talk by saying, “I am a strong believer that cooperation and collaboration, whatever it is, between government and industry can work … and has had an impact on how research and development in the semiconductor industry is done everywhere in the world today.” SOURCES: Spencer presentation (February 9, 2010) and IOM, 2007. can be made” that make sense of the data, Friend said. Bryn Williams-Jones, associate research fellow and head of eBiology at Pfizer, concurred, noting that “in spite of knowing a lot more and having a lot more data to go on, we are actually getting worse at finding out anything, and are not much more productive.” He called for more data analysis standards so that more valid conclusions can be drawn from the data acquired. Developing such standards will require a collaborative effort. Williams-Jones suggested that companies should not develop their
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary FIGURE 1 30-year decline in new molecular entities per dollar spent on research and development (R&D). There has been a 30-year decline in pharmaceutical industry productivity, as measured by new molecular entities per dollar spent on R&D, normalized to 5-year rolling average of 1970 to 1975. While research and development costs have increased 50-fold during this time period, the output of investigational new drug candidates and new drug application products has stayed flat. NOTE: NME = new molecular entity. SOURCES: Wagner presentation (February 9, 2010) and Booth and Zemmel (2004). Reprinted by permission from Macmillan Publishers Ltd: Nature Reviews Drug Discovery, Booth, B., and R. Zemmel. 2004. Prospects for productivity. 3(5):451–456, copyright 2004. own costly information technology infrastructures, but rather join a collaborative endeavor that provides that infrastructure, ideally in the virtual public domain. “We are a drug discovery industry, and none of us can afford to reinvent and source an entirely proprietary software system that is going to be able to help us deal with this. We should be doing that in the public domain,” he said. “Even for Pfizer, which has one of the world’s largest R&D budgets, it would be naïve to expect that we have wide enough domain expertise. We should focus our time thinking about what is and what isn’t competitive…. As we stand at a crossroad, expecting lots more data to come with not much more money to spend on it, we ought to think about whether we are going to continue internalizing or move into
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary FIGURE 2 Advances inmolecular biology, functional genomics, and genetics have fueled an explosion of data. Computational methods for integrating massive molecular and clinical datasets are needed to create predictive disease models that can recapitulate complex biological systems, according to Friend. Models can inform understanding of disease causality and can generate new mechanisms, targets, diagnostics, and knowledge. NOTE: BCE = before common era, DARPA = Defense Advanced Research Projects Agency, GB = gigabyte, PB = petabyte, TB = terabyte. SOURCE: Friend presentation (February 9, 2010). Reprinted, with permission, from Eric Schadt and Stephen Friend.
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary the virtualization phase. Given that we have all built these overlapping very similar IT systems, if we did this once properly in the public domain, that might actually cause a rising tide that floats all the [pharmaceutical industry] boats,” Williams-Jones added. A collaborative effort is also needed to create complex models of the causes and treatment targets of cancers, Friend said. Drug development models that depend on simple pathway approaches are no longer appropriate, he pointed out, because studies indicate that when one pathway that fuels cancer growth is blocked, a redundant pathway will enable the cancer to thrive. He showed one slide that illustrated the complex transcriptional networks involved in the growth of brain tumors (see Figure 3) and stressed that “people are recognizing that these cells and disease states are intricately FIGURE 3 A network of transcription factors (boxes) and their mesenchymal gene expression signature targets (circles) involved in high-grade glioma. SOURCES: Friend presentation (February 9, 2010) and Carro et al. (2010). Reprinted by permission from MacMillan Publishers Ltd: Nature, Carro, M. S., W. K. Lim, M. J. Alvarez, R. J. Bollo, X. Zhao, E. Y. Snyder, E. P. Sulman, S. L. Anne, F. Doetsch, H. Colman, A. Lasorella, K. Aldape, A. Califano, and A. Iavarone. 2010. The transcriptional network for mesenchymal transformation of brain tumours. 463(7279):318–325, copyright 2010.
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary wired networks that are brilliantly built through evolution with redundancy, which is why so often a drug does not work the way you thought it would, and does all those things you hadn’t expected.” He added that given their complexity, “no one company is going to be able to afford to have the best map of those networks for very long, even if they invest heavily.” David Wholley, director of the Biomarkers Consortium, agreed with Friend that the bigger picture required to understand and treat cancer is causing a paradigm shift in biomedical research and drug development that requires new approaches. “The increasing complexity, amount of data, and downstream effects on regulatory science are leading to the dawning realization that nobody is smarter than everybody else,” Wholley said. Neal Cohen, vice dean and professor at the University of California–San Francisco (UCSF) School of Medicine, added that biomedical research is increasingly a multidisciplinary venture dependent on much more difficult research methodology, both of which fuel the need for more collaboration. To be successful, he noted, individual investigators increasingly rely on collaborators to gain expertise outside an individual investigator’s discipline. Karim Lakhani, an assistant professor at Harvard Business School, concurred, pointing out how knowledge is unevenly and widely distributed so that “no one organization or set of actors can monopolize knowledge…. This is the fundamental problem we face in our pharma business now. If you think about the explosion in research, the specialization that happens across disciplines, there is no way we can just be in our little silo and innovate, especially when diseases are multicategorical, multisymptomatic, and multicausal. We need to think of new ways to access this type of knowledge. We have reached the limits and we have to work together because you can’t do it alone.” Lakhani noted that the widely distributed nature of knowledge is also evident within Joy’s law,2 which states: “No matter who you are, most of the smartest people work for someone else.” To illustrate the distributed nature of knowledge, he gave the example of Robert Langer, an expert in tissue engineering at Massachusetts Institute of Technology (MIT). Langer collaborated with about 40 percent of the most prolific authors of journal articles on the topic during 2004–2006. Although Langer is central in the domain of his field, his publications were only a fraction of the 6,000 articles published on the topic in that 2-year period by 17,000 authors in 2 Attributed to Bill Joy, cofounder of Sun Microsystems and lead technical contributor to TCP/IP, Berkeley Unix, Sparc, and Java.
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary the field, a network map constructed by Lakhani showed (see Figure 4). “There is no way that Langer or his team can know exactly what is going on in the entire domain,” Lakhani said. Another of Lakhani’s network maps showed that the number of publications by a science team at a large pharmaceutical company was dwarfed by the multitude of publications by other research teams in the field. “They thought that they were the cat’s meow in this area of neuroscience, but they were really quite marginal,” said Lakhani. “This is the problem faced by most organizations—most of the smartest people don’t work for them.” Patient Variability Friend stressed that most standard-of-care cancer treatments available today are effective in only a minority of patients, in part because of the tremendous variability in the molecular abnormalities driving tumor formation (IOM, 2007; PCAST, 2008; Spear et al., 2001), which standard drug trials do not consider. Those clinical trials that do try to account for such variability with standard trial designs often need thousands of volunteers, which make the clinical trials costly, risky, and lengthy, noted Laura Esserman, director of the Carol Franc Buck Breast Care Center at UCSF. She pointed out that breast cancer has several different subsets of the disease that respond differently to the same breast cancer drugs. “If you are not able to use a biomarker to tell you how to subset that patient population or to target [their specific disease], you are going to need 10 times as many patients to get an answer, and you are more likely to miss the benefits of certain drugs. We have turned breast cancer into a group of orphan diseases, and that is really going to be the step forward for every disease,” Esserman said. Cohen added that “there is great interest in comparative effectiveness studies to assess which therapies are most appropriate for individual patients, and to define personalized approaches to clinical management. All of that is very different from what we have done in the past, and necessitates a different model.” Mark McClellan, director of the Engelberg Center for Health Care Reform at the Brookings Institution, concurred that an absence of validated markers for patient subsets makes trials longer and less predictable, as does an absence of validated disease models. This hampers the development of innovative therapies and leads to more company efforts being spent devel-
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary FIGURE 4 Distributed nature of knowledge. A network map of all authors who published 2 or more articles on tissue engineering from 2004 to 2006 included more than 17,000 authors and 6,000 articles. Authors highlighted in green have more than half of their articles coauthored by Robert Langer, an expert in tissue engineering. Central in the domain of his field, Langer collaborated with 40 percent of the most prolific authors; however, his publications represent only a fraction of the articles published during this 2-year time period. SOURCE: Lakhani presentation (February 9, 2010).
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary Standards and Quality Control Some speakers suggested developing and using data standards and data elements to facilitate collaborations. Woosley suggested using the data standards of the Clinical Data Interchange Standards Consortium. Esserman added that for the I-SPY 2 TRIAL, “it is really critical to have common data elements for easier integration of clinical imaging and molecular results so you can send stuff around.” Standard research methods and procedures also must be followed. “If you are really going to do team science, everyone has to adhere to standards, even the people who think they are the most expert,” said Esserman. “We learned to send people around and certify every site to make sure they are doing everything right.” She suggested making sure there is a quality improvement design built into research collaborations. Heywood also stressed the importance of optimizing collaborative research to ensure data quality. NEXT STEPS Participants at the conference suggested several next steps to take to foster precompetitive collaborations, including Seeking public support for collaborations and advocating for funding; Holding a meeting with key constituents in oncology to determine how to move the field forward; Having an appropriate authoritative body establish a set of standards for the sharing of data, material, and tools, and/or general standards for collaboration; Publicizing collaboration success stories and management plans; and Developing innovative business models for collaborations. Seek Public Support for Collaborations Some speakers offered specific suggestions for fostering more public support and funding for collaborative ventures. McClellan suggested that policies financially reward the development of shared data repositories and infrastructure for effective collaboration. Such incentives could include direct payments (i.e., government funding) for infrastructure or for participation and reporting of data, as well as payments for achievement of well-defined outcomes. McClellan echoed Woosley’s request that more funding
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary be given to FDA so it can participate in more collaborations and that more public funding be available, in general, to support collaborative ventures. “These issues are fundamentally important to making better treatments available and should be much higher on the list of public health concerns of the nation than they actually are,” said McClellan. McClellan also suggested that patients could give more momentum to collaborative efforts by advocating for them and framing the issues in a way that could potentially garner more public support and funding. Woosley concurred, pointing out that “there is a huge opportunity now to get the disease groups to speak with one voice, and to talk about precompetitive sharing for all diseases. A lot of disease foundations are setting up venture philanthropy organizations to fund the kind of business initiatives that they want, and they are very concerned that the basic research has not been translated into business opportunities, which are how the patients really get the final benefit.” Friend added, “I am struck by the emerging role of disease foundations as engines for therapies. They have more of a voice now. We need to highlight patient advocates and disease foundations and the roles they can play.” Friend added that patients can play an important advocacy role, especially in fostering the collaborations needed to further personalized medicines. “The patients can say ‘why isn’t this drug working in me?’ and can collect the data if others aren’t collecting the data.” Establish Collaboration Standards and Incentives Attendee Richard Bookman, vice provost for research at the University of Miami Leonard M. Miller School of Medicine, suggested that an appropriate authoritative body devise a set of standards on the sharing of data, materials, tools, and collaboration that federal, state, and other funding agencies of biomedical research could use as guidance when shaping their grant programs. Woosley suggested advocating that the implementation of electronic medical records that will soon be supported by the federal government stimulus bill include common data elements that could be useful in research and would help ease research collaborations. Publicize Collaboration Success Stories and Management Plans McClellan suggested publicizing collaborations that have been done successfully and others that show promise, as well as the specific pathways for doing collaborations effectively. Several participants suggested putting the
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary management plans that have been drafted for some of the collaborations discussed at the workshop into the public domain, if possible, so that others can learn from them. Wholley noted that the project management plan template for the Biomarkers Consortium is posted on the Web,14 and Williams-Jones said that the model grant agreement for the Innovative Medicines Initiative can be accessed from the Internet.15 Esserman agreed with Friend’s suggestion that the IP and data-sharing collaborative agreements that have been forged by the I-SPY 2 TRIAL be distributed more widely and combined with efforts by Science Commons and others to build similar standard agreements, noting that “everything we built in this trial can be reused.” As an incentive for industry and academia to use the START clauses when collaborating, Murphy said the CEOs of the Roundtable on Cancer are trying to develop some sort of public acknowledgment of those companies and institutions that use the clauses and how the clauses have accelerated the clinical trial development process. Develop Innovative Business Models Some speakers suggested devising innovative business models that support collaborations. Woosley suggested creating “innovative ways for companies to come together to pool their diagnostics and drugs, and to develop more comprehensive strategies, rather than just a single agent.” Esserman noted that for the I-SPY 2 TRIAL she had to come up with a new business model to support the trial because she did not want to adhere to the old model of having a drug company, whose drug was being tested, financially back the study. Instead, the goal was to have broader based support, which is likely to benefit many stakeholders, including the drug industry. Although the funding for the trial is still being worked out, some drug companies are opting to help fund the trial even though they are not contributing molecules to be tested. Lakhani discussed what type of business model is needed for precompetitive research and compared that to the traditional business model. Lakhani said the traditional innovation model that most pharmaceutical firms use is the private innovation model, in which firms make private investments to solve technical problems and expect monopoly returns for 14 See http://www.biomarkersconsortium.org/index.php?option=com_content&task=section&id=7&Itemid=41. 15 See http://imi.europa.eu/calls-02-stage2_en.html.
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary their successful innovations. IP is jealously guarded and patented in this model, and sharing of knowledge only happens through accidental spillovers, that is, from employees with the knowledge switching companies. In contrast, in a collective innovation model, which typically involves government funding of research, individuals get external subsidies to solve technical problems and the knowledge they acquire in the process is given to a common pool for reuse and creative recombination. In this model, parties self-regulate through norms such as reciprocity, recognition, and peer esteem. But free riding is a central concern and can occur. What is beginning to emerge, according to Lakhani, is a private–collective hybrid model, in which firms and individuals exert private effort but disclose their work to others in a common pool. Innovators get selective benefits through participation that outweigh the cost of investment. These benefits include access to new knowledge and new materials, access to people, and shared risk. Participants can combine the knowledge from the common pool with their own specific and proprietary assets to create value, and free riders cannot share in the selective benefits. SEMATECH is a successful example of the private–collective innovation model, according to Lakhani (see Table 2). There was some debate at the workshop about whether a large-scale SEMATECH-like umbrella effort should be made to support collaborations in biomedicine or more specifically in oncology, or whether support should be focused on individual collaborative projects and consortia. Spencer noted that a SEMATECH-like umbrella organization and source of funding could reduce the significant amount of time they spend trying to garner funding for and supporting their collaborative efforts. “You have got a whole series of projects where if you shared that activity you could cut down the time, the administrative overhead, that each of these principal investigators or heads of projects has to allow them to get on with the real work of getting something done,” he said. “Oncology could take the lead in looking at something that was funded by the government and by private industry, and you have got a CEO Roundtable already, so you are light years ahead of where we were in the semiconductor industry in 1985 when we were trying to get SEMAT-ECH rolling.” Cohen pointed out that a centralized support organization for collaborations in oncology or biomedicine could not only support scientific pursuits, but also, or instead, efforts to develop new regulatory pathways for collaboratively developed drugs and other public policy advances needed to support collaborative research.
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary TABLE 2 Innovation Models Private Innovation Model Collective Innovation Model Private–Collective Hybrid Model Firms make private investments to solve technical problems—is often risky Individuals get external subsidies to solve technical problems Firms/Individuals exert private effort, but disclose work to others in a common pool Expect monopoly rents for their successful innovations Knowledge is given to a common pool for reuse and creative recombination Innovators get “selective benefits” through participation that outweigh the cost of investment: Access to novel knowledge; Access to new materials; Access to people; and Shared risk Innovation outcomes are rival and excludable Parties self-regulate through norms like reciprocity, recognition, and peer esteem Participants can combine knowledge from common pool with own specific and proprietary assets to create value Sharing of knowledge only happens through “accidental” spillovers Free riding becomes a central concern Free riders cannot share in the selective benefits SOURCE: Lakhani presentation (February 9, 2010). Munos countered that a centralized approach to fostering collaborations in biomedicine has the danger of squelching the diverse, creative collaborative approaches that are currently undertaken and surviving on shoestring budgets. “Rather than feeding a lot of money in the system, I would feed some money into those people who are coming up with experimental models. Most of them will fail, but those that succeed could prove to be very disruptive of the traditional pharma R&D model, and might be able to renew it in ways that would be pretty healthy,” Munos said. Altshuler said there is a false dichotomy of centralized support versus support for more entrepreneurial ventures. “Certain types of problems can be handled one way, and certain ones can be handled another way. Is there
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary a role going forward for some central funding that can set priorities and not necessarily encompass that which is already going on?” she asked. Woosley noted the current significant consortium fatigue among those participating in collaborations and stressed that “any efforts that we come up with should really focus on coordinating and getting maximum benefit from all the dollars that are already in place before we ask for more, because I think we are going to get a lot of resistance from an industry that is really downsizing its research efforts right now.” Williams-Jones concurred, and suggested seeing beyond the spectrum of oncology, and considering other disease areas as a way to tap into funding for collaborative research and relieve consortium fatigue. “We really need a global solution that will allow us to spend the little cash that we do have to spread across this,” he said. Mendelsohn pointed out that different collaborative efforts may require different approaches to tackle them, and that the SEMATECH approach may not be appropriate for all of them. “We need to attack the issues of regulat[ion], infrastructure, and interoperable datasets, and we have to attack the issue of how to make biomarker-driven drug selection trials work. Those are three different problems that may require different approaches,” he said. SUMMARY After 2 days of presentations and lively discussion, during which Washington, DC, was blanketed in a crippling snowstorm, it became apparent that a number of factors are currently driving precompetitive collaborations, including declining R&D budgets combined with the growing complexity of biomedical research. Several participants viewed precompetitive collaboration as a means to solve some of the problems that currently plague the drug development process both in oncology and in other therapeutic areas. Speakers also noted that precompetitive collaborations have to be crafted carefully to provide incentives and rewards to participants while avoiding legal, cultural, technical, and other obstacles. Innovative regulations and business plans may foster precompetitive collaborations and enable their products to seamlessly enter the market. Speakers discussed many lessons that can be learned from the precompetitive collaborations that have been attempted and/or accomplished successfully, including the importance of starting a collaboration by defining its goals and planning for how those goals can be accomplished, bring-
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Extending the Spectrum of Precompetitive Collaboration in Oncology Research: Workshop Summary ing together the right stakeholders early in the planning process, actively managing the collaboration, and using a trusted third party to foster collaborations. Speakers also suggested that critical legal issues be addressed, such as intellectual property and patents, the sharing of data, conflict of interest, and antitrust issues. To further precompetitive collaboration, speakers suggested several next steps. Some ideas included: seeking more public support and funding for collaborations, publicizing collaboration success stories and management plans, and having an appropriate authoritative body establish a set of standards for sharing precompetitive materials. REFERENCES Abrams, J., R. Erwin, G. Fyfe, and R. L. Schilsky. 2010. Data submission standards and evidence requirements. The Oncologist 15(5):488–491. Barker, A., C. C. Sigman, G. J. Kelloff, N. M. Hylton, D. A. Berry, and L. J. Esserman. 2009. I-SPY 2: An adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clinical Pharmacology & Therapeutics 86(1):97–100. Boat, T. F. 2010. Insights from trends in biomedical research funding. Journal of the American Medical Association 303(2):170–171. Booth, B., and R. Zemmel. 2004. Prospects for productivity. Nature Reviews Drug Discovery 3(5):451–456. Carro, M. S., W. K. Lim, M. J. Alvarez, R. J. Bollo, X. Zhao, E. Y. Snyder, E. P. Sulman, S. L. Anne, F. Doetsch, H. Colman, A. Lasorella, K. Aldape, A. Califano, and A. Iavarone. 2010. The transcriptional network for mesenchymal transformation of brain tumours. Nature 463(7279):318–325. CEO Life Sciences Consortium. 2010. START clauses. http://ceo-lsc.org/TaskForceScrape.aspx (accessed April 19, 2010). CEO Roundtable on Cancer and NCI (National Cancer Institute). 2008. Proposed standardized/harmonized clauses for clinical trial agreements. Rockville, MD: CEO Roundtable on Cancer and NCI. Clark, A., M. Ellis, C. Erlichman, S. Lutzker, and J. Zwiebel. 2010. Development of rational drug combinations with investigational targeted agents. The Oncologist 15(5):496–499. Critical Path Institute. 2010a. About us. http://www.c-path.org/about.cfm (accessed April 20, 2010). Critical Path Institute. 2010b. Financial status. http://www.c-path.org/funding.cfm (accessed April 20, 2010). Dilts, D. M., and A. B. Sandler. 2006. Invisible barriers to clinical trials: The impact of structural, infrastructural, and procedural barriers to opening oncology clinical trials. Journal of Clinical Oncology 24(28):4545–4552. DOJ (Department of Justice). 2008. Response to the CEO Roundtable on Cancer’s request for business review letter. http://www.justice.gov/atr/public/busreview/237311.htm (accessed April 19, 2010).
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